michelle vonahn, ruth lupton and dick wiggins

Michelle vonAhn, Ruth Lupton and Dick Wiggins

Population, language, ethnicity and socio-economic aspects of education

Aims of the fellowshipAnalyse and map distribution of language across

LondonWhat issues does this raise?

Conduct some preliminary analysis between language and attainment

Analyse the relationship between language, ethnicity and socio-economic indicators

Provide guidance and training on the ways language data may be used with other data to answer social and educational research questions

A big issue in London

Updating Multilingual CapitalPublished in 2000, using pupil data from 1999 to identify and map languages in London

Pupil data 1999 2008

Pupils >850,000, attending state schools in London

>1,100,000, resident in London, attending a state school

Languages >350, including dialects and variants

322 categories collected, 239 without variants

Geography Boroughs mainly, some postcodes

Boroughs and MSOAs

Missing data Bromley and Havering did not collect data – synthetic data used

Variable data collection between schools and local authorities

But data collection variability makes comparison difficult…

Language data ambiguityCategories include: % of London total

Missing data 0.6%

Not obtained 0.4%

Classification pending 0.3%

Refused 0.1%

Other language 0.4%

Other than English 4.5%

Believed to be other than English 1.3%

Believed to be English 0.8%

Total ambiguous 8.4%

Ambiguous language

Borough Total pupils % ambiguous

Westminster 16,086 27.9%

Brent 43,120 21.1%

Waltham Forest 38,500 15.6%

Haringey 35,056 14.5%

Hounslow 35,203 14.0%

Newham 50,402 12.4%

…

Havering 33,526 2.5%

Ealing 46,511 2.3%

Data inconsistencySome languages have variants, which are not consistently used within a

local authority or across London, e.g.

Bengali Panjabi Arabic Chinese

Bengali (Any other) Panjabi (Any other) Arabic (Any other) Chinese (Any other)

Bengali (Sylheti) Panjabi (Gurmukhi) Arabic (Algeria) Chinese (Cantonese)

Bengali (Chittagong/Noakhali) Panjabi (Mirpuri) Arabic (Iraq) Chinese (Hokkien/Fujianese)

Panjabi (Pothwari) Arabic (Morocco) Chinese (Hakka)

Arabic (Sudan) Chinese (Mandarin/Putonghua)

Arabic (Yemen)

Asian

(South)Asian (East)

Asian (W/C)

Turkish

African (North)

Africa (West)

Africa (E/C/S)

European Union

European Other

International/ Transnational Other

Bengali

Urdu

Panjabi

Gujarati

Tamil

Hindi

Malayalam

Nepali

Other

Chinese

Viet-namese

Japanese

Korean

Tagalog/Filipino

Other

Persian/ Farsi

Kurdish

Pashto/Pakhto

Other

Somali

Tigrinya

Amharic

Other

Yoruba

Akan/Twi-Fante

Igbo

Other

Lingala

Swahili/ Kiswahili

Luganda

Shona

Other

Greek

Italian

Dutch/Flemish

German

Polish

Lithuanian

Other

Albanian/Shqip

Russian

Serbian/Crotian/Bosnian

Other

Arabic

French

Portu-guese

Spanish

CaribbeanCreoles

Oceania/S/C America

Unspeci-fied

>5000pupils

Language classification

Geography• Percentage comparisons are problematic due to data capture variability

• Comparative counts of boroughs not suitable due to differences in size

• Wards and postcodes also differ in population size

•New statistical geographies - Super Output Areas

LSOA MSOA

4765 in London 983 in London

About 1500 people About 7500 people

LSOA map

MSOA map

English and Believed to be English

English and Believed to be English

Choosing a scale

Equal counts

Aims for equal numbers of MSOAs in each category

Hides extreme values

Equal ranges

Aims to divide the whole range into equal segments

Extreme values dominate

Natural break

Elegantly captures both intensity and distribution

Complex mathematics not made explicit by MapInfo, and therefore difficult to explain to non-expert viewers

Quantiles (or in this case, Quintiles!)

Takes total count of pupils and creates target totals for each category – so each category has about 20% of all pupils

A compromise that captures intensity and distribution, relatively easy to explain

Patterns of clustering and dispersal

South Asian languages

Bengali/Sylheti, 1999

Bengali

London = 46,681

Hindi/Urdu, 1999

Urdu

London = 29,354

Panjabi

London = 20,998London = 20,998

Gujarati

London = 19,572

Tamil

London = 16,386

Persian/Farsi

London = 6,959

Chinese

London = 5,905

Migration patterns over timeAnnual data could show change (if data is collected in a

robust way)Established or magnet communitiesRecent arrivals

Turkish, 1999

Turkish

London = 16,778

Greek

London = 3,336

Polish

London = 11,035

Lithuanian

London = 2,974

Somali

London = 27,126

Somali numbers have increased, but their distribution has also become more dispersed

Language is not always enoughFrench speakers

17% White57% Black26% Other

Arabic speakers57% Other15% Black10% Mixed9% White8% Asian

Spanish speakers

35% White4% Black61% Other

Portuguese speakers54% White19% Black27% Other

French by ethnic group

London = 13,020

French has an east-west distribution by ethnic group

smaller numbers

Spanish by ethnic group

London = 8,647

White Spanish speakers are more likely to be from Europe, while Other Spanish are probably from Central and Latin America

Language, ethnicity and attainment

How are ethnicity and language related? Can we create useful ethnicity/language categories?

How is language related to attainment? Does ethnicity/ language tell us more than ethnicity on its own?

Average points at Key Stage 2 by Ethnic Group (London 2008)

Linguistic Breakdown for Selected Lower Attaining Groups

Language N % of total

Bengali 3725 92%

Other than English 205 5%

Believed to be English 69 2%

Others ( 10 or less each) 47 1%

Bangladeshi

Language N % of totalEnglish/Believed to be English 1097 66%

French 86 5%

Other than English 68 4%

Portuguese 61 4%

Yoruba 57 3%

Somali 49 3%

Arabic 37 2%

Akan 30 2%

Swahili variants 18 1%

Creoles and Pidgins 14 1%

Lingala 14 1%

Unknown 12 1%

Others (10 or less each) 118 7%

Black ‘other’

Language N % of total

English/Believed to be English 2481 25%

Somali 2079 21%

Yoruba 1245 13%

Akan 682 7%

French 502 5%

Lingala 259 3%

Igbo 220 2%

Arabic 181 2%

Swahili variants 183 2%

Luganda 112 1%

Portuguese 131 1%

Black African

Language N % of total

English/Believed to be English 1887 26%

Turkish 1184 16%

Polish 757 11%

Albanian/Shqip 559 8%

Portuguese 505 7%

Greek 263 4%

Spanish 199 3%

Lithuanian 237 3%

French 116 2%

Italian 151 2%

Arabic 119 2%

Serbian/Croatian/Bosnian 100 1%

Russian 107 1%

White ‘other’

Linguistic Breakdown for Selected Lower Attaining Groups

Lower attaining Higher attaining

Diversity in the ‘Black African’ group

70 75 80 85 90

Chinese

Indian

Black AfricanIgbo

White British

Black AfricanYoruba

Black AfricanEnglish

All

70 75 80 85 90

All

Black African Luganda

Bangladeshi

Pakistani

Black African Akan

Black African Arabic

Other White

Other

Black African all

Black Caribbean

Black Other

Black African Swahili variants

Black African Somali

Black African Portuguese

Black African French

Black African Lingala

Yoruba

London = 13,961

Igbo

London = 2,837

Akan/Twi/Fante

London = 8,117

Higher attaining

Diversity in the ‘white other’ group Lower attaining

70 75 80 85 90

White Other French

Chinese

White Other Italian

White Other Serbian Croatian Bosnian

White Other Greek

White Other English

Indian

White Other Spanish

White British

All

70 75 80 85 90

All

White Other Arabic

White Other Russian

Bangladeshi

Pakistani

White Other Albanian

White Other Arabic

Other White

Other

White Other Lithuanian

White Other Portuguese

White Other Turkish

White Other Polish

Next stagesHow are ethnicity/language categories related to socio-

economic status?Explore FSM, IDACI, using London ASCMatching to local authority data (e.g. housing benefits,

Council tax band), for a case study borough (Newham)

How is the attainment of ethno-linguistic groups related to indicators of socio-economic status?

Data matching

GP register of patients

GP register of patients

Council Tax

Housing benefit

Electoral RegisterPLASC

(FSM)

LLPG addresses

Attainment and

language data

ConsultationLocal authority views of the practical, legal, technical and

ethical issues for data matching within and across authorities

Identifying practical uses of matched data

Goal: to prepare guidance for other data users

Michelle vonAhnEmail: michelle.von.ahn@newham.gov.ukTel: 020 3373 1659

Ruth LuptonEmail: r.lupton@lse.ac.ukTel: 0207 849 4910